Analysing accuracy, balancing bias: Can ChatGPT be used to ease the care documentation burden?
GoLTC Data Science Group
May 2023
GOLTC Data Science
- GOLTC Introduction Adelina Comas-Herrera
- Data Science Interest Group: Structure and webinars
- Steering Group: Sam Rickman (CPEC at LSE), Jiyoun Song (UPenn), Tommy Henderson-Reay (NHS England), Sarthak Saluja (CPEC at LSE)
- Racial disparities and harmful hallucinations in automated speech recognition Allison Koenecke, Cornell Department of Information Science
- Adapted large language models can outperform medical experts in clinical text summarization Dave Van Veen, Stanford Center for Artificial Intelligence
Documentation burden
- An assessment or intervention takes place with a person using care services.
- Later the worker types up a summary on a case recording system.
- 6 - 20 hours per worker per week spent on writing documentation:
- Reduced time to spend care and assessment.
- High levels of burnout across professions.
Case study: Magic Notes
- Conversation is recorded using a worker’s phone.
- This audio file is transcribed into text using an AI speech-to-text model.
- This transcript is summarised into a record of the meeting using an AI summarisation model, such as GPT4.
Today’s webinar: moderated discussion
- Disparities in automated speech recognition (Allison Koenecke, Cornell Department of Information Science)
- Adapted large language models can outperform medical experts in clinical text summarization (Dave Van Veen, Stanford Center for Artificial Intelligence)